Modern-day hearing aids, when employing different audiophonic programs—typically two to a maximum of three such hearing programs—permit their adaptation to varying acoustic environments or scenes. The idea is to optimize the effectiveness of the hearing aid for its user in all situations.
The hearing program can be selected either via a remote control or by means of a selector switch on the hearing aid itself. For many users, however, having to switch program settings is a nuisance, or difficult, or even impossible. Nor is it always easy even for experienced wearers of hearing aids to determine at what point in time which program is most comfortable and offers optimal speech discrimination. An automatic recognition of the acoustic scene and corresponding automatic switching of the program setting in the hearing aid is therefore desirable.
There exist several different approaches to the automatic classification of acoustic surroundings. All of the methods concerned involve the extraction of different characteristics from the input signal which may be derived from one or several microphones in the hearing aid. Based on these characteristics, a pattern-recognition device employing a particular algorithm makes a determination as to the attribution of the analyzed signal to a specific acoustic environment. These various existing methods differ from one another both in terms of the
characteristics on the basis of which they define the acoustic scene (signal analysis) and with regard to the pattern-recognition device which serves to classify these characteristics (signal identification).
For the extraction of characteristics in audio signals, J. M. Kates in his article titled “Classification of Background Noises for Hearing-Aid Applications” (1995, Journal of the Acoustical Society of America 97(1), pp 461-469), suggested an analysis of time-related sound-level fluctuations and of the sound spectrum. On its parts, the European patent EP-B1-0 732 036 proposed an analysis of the amplitude histogram for obtaining the same result. Finally, the extraction of characteristics has been investigated and implemented based on an analysis of different modulation frequencies. In this connection, reference is made to the two papers by Ostendorf et al titled “Empirical Classification of Different Acoustic Signals and of Speech by Means of a Modulation-Frequency Analysis” (1997, DAGA 97, pp 608-609), and “Classification of Acoustic Signals Based on the Analysis of Modulation Spectra for Application in Digital Hearing Aids” (1998, DAGA 98, pp 402-403). A similar approach is described in an article by Edwards et al titled “Signal-processing algorithms for a new software-based, digital hearing device” (1998, The Hearing Journal 51, pp 44-52). Other possible characteristics include the sound level itself or the zero-passage rate as described for instance in the article by H. L. Hirsch, titled “Statistical Signal Characterization” (Artech House 1992). It is evident that the characteristics used to date for the analysis of audio signals are strictly based on system-specific parameters.
One shortcoming of these earlier sound-classification methods, involving characteristics extraction and pattern recognition, lies in the fact that, although unambiguous and solid identification of speech signal is basically possible, a number of different acoustic situations cannot be satisfactorily classified, or not at all. While these earlier methods permit a distinction between pure speech signals and “non-speech” sounds, meaning all other acoustic surroundings, that is not enough for selecting an optimal hearing program for a momentary acoustic situation. It follows that the number of possible hearing programs is limited to those two automatically recognizable acoustic situations or the hearing-aid wearer himself has to recognize the acoustic situations that are not covered and manually select the appropriate hearing program.
It is fundamentally possible to use prior-art pattern identification methods for sound classification purposes. Particularly suitable pattern-recognition systems are the so-called distance classifiers, Bayes classifiers, fuzzy-logic systems and neural networks. Details of the first two of the methods mentioned are contained in the publication titled “Pattern Classification and Scene Analysis” by Richard O. Duda and Peter E. Hart (John Wiley & Sons, 1973). For information on neural networks, reference is made to the treatise by Christopher M. Bishop, titled “Neural Networks for Pattern Recognition” (1995, Oxford University Press). Reference is also made to the following publications: Ostendorf et al, “Classification of Acoustic Signals Based on the Analysis of Modulation Spectra for Application in Digital Hearing Aids” (Zeitschrift fur Audiologie (Journal of Audiology), pp 148-150); F. Feldbusch, “Sound Recognition Using Neural Networks” (1998, Journal of Audiology, pp 30-36); European patent application, publication number EP-A1-0 814 636; and U.S. Pat. No. 5,604,812. Yet all of the pattern-recognition methods mentioned are deficient in one respect in that they merely model static properties of the sound categories of interest.